The present invention relates to an image detection apparatus for detecting a roundish shape, specifically a rounded convex region such as a malignant tumor, from an image of part of a human body photographed by X-ray photography and a detection method thereof.
A technique for recognizing a malignant tumor from an image taken by, for example, X-ray photography is normally executed with the following two-stage processing. In the first stage,.candidate malignant tumor points are extracted by filter processing. In the second stage, coupling processing is carried out and then normal regions which have been false-positive are deleted with the candidate malignant tumor remaining.
The first conventional technique for realizing those first and second stages will be described.
As a filter for extracting a malignant tumor in the first stage, a Min-DD filter for outputting a minimum value from second order directional differential filter outputs in all directions is known.
The Min-DD filter has an excellent function of controlling image regions of vein regions such as blood vessels and relatively emphasizing a malignant tumor of the type which is constituted only of a pixel intensity surface in which gradients change relatively greatly in all directions. A pixel intensity surface is a graph in which an image is positioned in the x-plane and the y-plane and the pixel intensity is plotted in the z-axis direction. Refer to Systems and Computers in Japan, Vol. 26, No. 11, pp. 38-51, 1995 for more detail.
Several methods for utilizing a neural network which classifies candidate malignant tumor region or images into malignancy and benignancy have been proposed. One of those methods is disclosed in detail in, for example, U.S. Pat. No. 5,463,548 (1995) by N. Asada and K. Doi.
As a method for deleting normal regions which have been false-positive in the second stage, the analysis of two-dimensional shapes including, for example, areas and roundness is carried out. In addition, regions are narrowed down using evaluation measures, such as the standard deviation of pixel intensity values within the regions and the contrast of the regions with their surroundings with reference back to the original image. This is disclosed in, for example, U.S. Pat. No. 5,579,360; 1996 by Mohamed. Abdel-Mottaleb in detail.
The aforementioned Min-DD filter functions to curb mammary glands and blood vessels while maintaining a malignant tumor of the type which consists only of a pixel intensity curved surface in which gradient changes are relatively great in all directions. At the same time, however, the filter curbs the pixel intensity curved surface even within a malignant tumor which has slight gradient changes in any directions. There exist, in fact, malignant tumors which have a pixel intensity curved surface in which gradient changes are slight in all directions.
Since the Min-DD filter curbs such portions, defects sometime occur at the time candidate malignant tumor points are extracted.
Moreover, it has never been proposed that a method using a neural network not for determination of the candidate regions but for determination of candidate points. In use of a neural network for the determination of candidate points, if the same teaching signals are given to the surrounding portion of a malignant tumor and points within the tumor and a different teaching signal is given to points which constitute the vein region, such as a blood vessel, then the efficiency of discriminating the constitutional points of the malignant tumor from those of the vein region such as a blood vessel decreases.
Furthermore, the candidate malignant tumor points extracted by the filter processing in the fist stage are extracted because they have similar features to those of the points constituting the malignant tumor. It has to be determined whether or not the pixel intensity curved surfaces in the coupled regions formed by those points are similar to the three-dimensional shape of the malignant tumor region.
In the conventional techniques as described above, the analysis of two-dimensional shape of the coupled region obtained as a binary value image and the statistical analysis of average pixel intensity values while referring back to the original image are carried out. However, the detailed examination of balances in the corresponding region using the differential information on the pixel intensity curved surface for purposes of analyzing the three-dimensional shape is not at all seen.
In case the points thus extracted form a small region, the difference between a circle and a rectangular shape peculiar to two-dimensional digital image processing is only slight and measures for evaluating two-dimensional shapes are not very effective.
This follows that it is required for a determination standard for a small region to be based on the three-dimensional shape on the pixel intensity curved surface.
Next, the second conventional technique for realizing the aforementioned first and second stages will be described.
First, first stage processing will be described. As a filter for extracting candidate malignant tumor points, there has been proposed an Iris filter which calculates the degree of the convergence of gradient vectors. The iris filter is excellent in extracting a rounded convex region, which content is disclosed in Proc. of the Int. Conf. on Image Processing, Vol. I, pp. 407-410, 1994. The degree of the convergence of gradient vectors is calculated through the following processing.
Using arrangements f1 to f16 of pixel intensity values as illustrated by FIG. 11, the gradient vector direction xcex8 is calculated. The calculation is based on the following mathematical expression:   θ  =            tan              -        1              ⁢                            (                                    f              3                        +                          f              4                        +                          f              5                        +                          f              6                        +                          f              7                                )                -                  (                                    f              11                        +                          f              12                        +                          f              13                        +                          f              14                        +                          f              15                                )                                      (                                    f              1                        +                          f              2                        +                          f              3                        +                          f              15                        +                          f              16                                )                -                  (                                    f              7                        +                          f              8                        +                          f              9                        +                          f              10                        +                          f              11                                )                    
Next, the convergence C of gradient vectors is calculated using the following mathematical expression:   C  =            (              1        /        N            )        ⁢                  ∑                  j          =          1                N            ⁢              xe2x80x83            ⁢              cos        ⁢                  xe2x80x83                ⁢                  θ          j                    
where symbol N denotes the total number of pixels within a circle with a radius of R.
Specifically, if a vector, having a noted point as a starting point and an arbitrary point a within neighboring regions as an end point, is defined as A and a vector indicating the gradient direction at the point a as G, then a cosine value of an angle made between the vectors A and G is calculated for every point within the neighboring regions and one point is obtained as their average. The Iris filter is characterized in that it does use only directions of gradient vectors and not magnitudes thereof and therefore does not depend on contrast. It is also characteristically designed such that filter size is variable appropriately in accordance with the magnitude of the malignant tumor.
Second stage processing will next be described. As regards the conventional method in which, after coupling processing has been carried out, normal regions (pseudo-malignant tumors) which have been false-positive are deleted with the malignant tumor remaining, there have been proposed an deletion method. In the deletion method, statistics on all pixels within regions, such as the average or variance of pixel intensity values within the regions, is taken into consideration. Alternatively, roundness of the two-dimensional shape is taken into consideration.
However, if applying the Iris filter stated above to, for example, mammography, the output of the neighboring regions of the malignant tumor tends to be high and the output of the shadow portion near the straight line of, for example, a mammary gland also tends to be relatively high. Due to this, if those pixels whose output values of the Iris filter are equal to or higher than a certain threshold value are picked up and all malignant tumor regions are intended to be extracted completely, then even the shadow portion near the straight line of, for example, a mammary gland is also false-positive.
Even if the degree of the convergence of gradient vectors used in the Iris filter is utilized so as to delete the false-positive candidate regions, the number of normal regions which have been false-positive cannot be reduced as expected.
Moreover, the conventional deletion method using statistics and the like, on all pixels within regions is capable of gradually deleting the false-positive regions. It is however, difficult to essentially delete the many false-positive regions.
It is therefore the first object of the present invention to provide an image detection apparatus and an image detection method capable of accurately discriminating a targeted roundish shape from a linear region or a background and extracting it from the inputted image, as well as a recording medium recording an image detection program of this type.
It is the second object of the present invention to provide an image detection apparatus and an image detection method capable of reducing the number of false-positive normal regions to a minimum without missing the extraction of the image of the targeted roundish shape from the inputted image.
To obtain the first object, an image detection apparatus according to the present invention comprises: a filter operation unit for conducting an operation of calculating a synthetic product between inputted image data and a plurality of filters, respectively, wherein the filters differ in direction; a directional and having respective orientations balance operation unit including an angle calculation unit for obtaining an angle made between a vector whose components are a plurality of outputs of the filter operation unit for each direction and a reference vector whose components are equal to each other; and a roundish shape detection unit for detecting roundish shapes within the inputted image data based on an output of the directional balance calculation unit.
To obtain the first object, an image detection method according to the present invention comprises: a filter operation step of calculating a conducting an operation of synthetic product between inputted image data and a plurality of filters, respectively, wherein the filters differ in direction; and having respective orientations a directional balance operation step including an angle calculation step of obtaining an angle made between a vector which components are a plurality of outputs of the filter operation unit for each direction and a reference vector which components are equal to each other; and a roundish shape detection step of detecting roundish shapes within the inputted image data based on an operation result of the directional balance calculation step.
To obtain the second object, an image detection apparatus for detecting a roundish shape from an inputted image according to the present invention comprises: a gradient vector calculation unit for, if the image is a three-dimensional curved surface consisting of pixel intensities and pixel positions on a two-dimensional coordinate, calculating a gradient vector of a pixel on a half-line with a pixel of interest defined as a starting point of the half-line, the gradient vector having a magnitude corresponding to a differential value of the pixel intensity on a pixel position and a direction corresponding to a direction perpendicular to a tangent plane on the three-dimensional curved surface; an orthogonal projection calculation unit for calculating an orthogonal projection of the gradient vector obtained by the gradient vector calculation unit to the half-line; a maximum length detection unit for detecting a maximum length of the orthogonal projection on each of the half-lines calculated by the orthogonal projection calculation unit; a similarity calculation unit for calculating a similarity between the maximum length of the orthogonal projection to the half-line and that to a different half-line detected by the maximum length detection unit; a correlation value calculation unit for obtaining a correlation value by adding the similarity between the maximum length of the orthogonal projection to one half-line and that to each of different half-lines calculated by the similarity calculation unit; and a maximum correlation value calculation unit for calculating the correlation value obtained by the correlation value calculation unit for all of the half-lines, finding a maximum correlation value of the correlation values and calculating a maximum value of the maximum correlation value when the pixel of interest moved thoroughly within the roundish shapes, wherein the roundish shapes are detected based on an output from the maximum correlation value calculation unit.
To obtain the second object, an image detection apparatus according to the present invention comprises: a gradient vector calculation unit for, if the image is a three-dimensional curved surface consisting of pixel intensities and pixel positions on a two-dimensional coordinate, calculating a gradient vector of a pixel on a half-line with a pixel of interest defined as a starting point, the gradient vector having a magnitude corresponding to a differential value of the pixel intensity on a pixel position and a direction corresponding to a direction perpendicular to a tangent plane on the three-dimensional curved surface; an orthogonal projection calculation unit for calculating an orthogonal projection of the gradient vector obtained by gradient vector calculation unit to the half-line; a maximum position detection unit for detecting a position at which the orthogonal projection of the half-line calculated by the orthogonal projection calculation unit reaches a maximum; a distance similarity calculation unit for calculating a distance between the maximum orthogonal projection position detected by the maximum position detection unit and the pixel interest and calculating a similarity of distances calculated for respective half-lines; a correlation value calculation unit for obtaining a correlation value by adding the distance similarity calculated for one half-line and that for each of different half-lines calculated by the similarity calculation unit; and a maximum correlation value calculation unit for calculating the correlation values obtained by the correlation value calculation unit for all of the half-lines, finding a maximum correlation value of the calculated correlation values and calculating a maximum value of the maximum correlation values when the pixel of interest is moved thoroughly within the roundish shapes, wherein the roundish shapes are detected based on an output from the maximum correlation value calculation unit.
To obtain the second object, an image detection method for detecting a roundish shape from an inputted image according to the present invention, a gradient vector calculation step of, if the image is a three-dimensional curved surface consisting of pixel intensities and pixel positions on a two-dimensional coordinate, calculating a gradient vector of a pixel on a half-line with a pixel of interest defined as a starting point, the gradient vector having a magnitude corresponding to a differential value of the pixel intensity on a pixel position and a direction corresponding to a direction perpendicular to a tangent plane on the three-dimensional curved surface; an orthogonal projection calculation step of calculating an orthogonal projection of the gradient vector to the half-line; a maximum position detection step of detecting a position at which the orthogonal projection of the half-line reaches a maximum; a distance similarity calculation step of calculating a distance between the maximum orthogonal projection position and the pixel of interest and calculating similarity of distances calculated for respective half-lines; a correlation value calculation step of obtaining a correlation value by adding the distance similarity calculated for one half-line and that for each of different half-lines; and a maximum correlation value calculation step of calculating the correlation values for all of the half-lines, finding a maximum correlation value of the calculated correlation values and calculating a maximum value of the maximum correlation values when the pixel of interest is moved thoroughly within the roundish shapes, wherein the roundish shapes are detected based on the maximum value obtained in the maximum correlation value calculation step.
To obtain the second object, an image detection method for detecting a roundish shape from an inputted image according to the present invention, a gradient vector calculation step of, if the image is a three-dimensional curved surface consisting of pixel intensities and pixel positions on a two-dimensional coordinate, calculating a gradient vector of a pixel on a half-line with a pixel of interest defined as a starting point of the half-line, the gradient vector having a magnitude corresponding to a differential value of the pixel intensities one-pixel positions and a direction corresponding to a direction perpendicular to a tangent plane on the three-dimensional curved surface; an orthogonal projection calculation step of calculating an orthogonal projection of the gradient vector to the half-line; a maximum length detection step of detecting a maximum length of the orthogonal projection on each of the half-lines; a similarity calculation step of calculating a similarity between the maximum length of the orthogonal projection to the half-line and that to a different half-line detected in the maximum length detection unit; a maximum position detection step of detecting a position at which the orthogonal projection of the half-line reaches a maximum; a distance similarity calculation step of calculating a distance between the maximum orthogonal projection position and the pixel interest and calculating a similarity of distances calculated for respective half-lines; a correlation value calculation step of obtaining a correlation value by adding a product of the maximum orthogonal projection length similarity and the distance similarity calculated for one half-line and that for each of different half-lines calculated in the similarity calculation unit and adding a resultant product; and a maximum correlation value calculation step of calculating the correlation value for all of the half-lines, finding a maximum correlation value of the correlation values and calculating a maximum value of the maximum correlation value when the pixel of interest is moved thoroughly within the roundish shapes, wherein the roundish shapes are detected based on the maximum value obtained in the maximum correlation value calculation step.
Additional objects and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objects and advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out hereinafter.